import torch
import torchvision
import torchvision.transforms as transforms

# load the cifar10 through torchvision.
transform = transforms.Compose([  transforms.ToTensor(), 
                                  transforms.Normalize(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)  ])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform = transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform = transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=True, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

print('done')



